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Understanding cities through city brands: City branding as a social and semantic network

Authors: Sevin, Efe;

Understanding cities through city brands: City branding as a social and semantic network

Abstract

More and more towns, cities, regions, and countries are investing in branding campaigns in order to establish a reputation for themselves, and to have a competitive edge in today’s global market. In their essence, branding campaigns are places’ attempts to define themselves to target audiences. However, the literature and practice of place branding have focused on the competition of brands at the expense of exploring the relations between people, symbols, meanings, and physical characteristics of cities. Therefore, current branding measurement scales and indices used to understand the defining characteristics of places are problematic. This article first analyzes three of the prominent place branding indices: Anholt-GfK Roper City Brands Index, FutureBrand Country Brand Index, and East–West Nation Brand Perception Index. Subsequently, it proposes an analytical framework combining two network analysis methods – social and semantic – to evaluate place brands, called “Define–Measure–Visualize” (DMV). In order to argue for the feasibility of the proposed method, a sample dataset is created based on tweets about Boston and New York City. By introducing a consumer-centric and communications-based approach and exploring the connection between cities, people, and messages, the findings of this research can be used in understanding cities/places, measuring the success of branding campaigns, and managing future campaigns.

Related Organizations
Keywords

social network analysis, semantic network analysis, measurement, place branding, city branding

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
117
Top 1%
Top 10%
Top 10%
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